Mfstomufs #43 (#44)

* Implement module mfs changed name to mufs

* Update github CI file

.github/workflows/main.yml

@@ -26,7 +26,6 @@ jobs:
           pip install -q --upgrade pip
           pip install -q -r requirements.txt
           pip install -q --upgrade codecov coverage black flake8 codacy-coverage
-          pip install -q git+https://github.com/doctorado-ml/mfs
       - name: Lint
         run: |
           black --check --diff stree

Makefile

@@ -26,6 +26,7 @@ doc:  ## Update documentation
 
 build:  ## Build package
 	rm -fr dist/*
+	rm -fr build/*
 	python setup.py sdist bdist_wheel
 
 doc-clean:  ## Update documentation

docs/requirements.txt

@@ -1,4 +1,4 @@
 sphinx
 sphinx-rtd-theme
 myst-parser
-git+https://github.com/doctorado-ml/stree
+mufs

requirements.txt

@@ -1,2 +1,2 @@
 scikit-learn>0.24
-mfs
+mufs

setup.py

@@ -44,7 +44,7 @@ setuptools.setup(
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Intended Audience :: Science/Research",
     ],
-    install_requires=["scikit-learn", "numpy", "mfs"],
+    install_requires=["scikit-learn", "mufs"],
     test_suite="stree.tests",
     zip_safe=False,
 )

stree/Splitter.py

@@ -12,7 +12,7 @@ from sklearn.feature_selection import SelectKBest, mutual_info_classif
 from sklearn.preprocessing import StandardScaler
 from sklearn.svm import SVC
 from sklearn.exceptions import ConvergenceWarning
-from mfs import MFS
+from mufs import MUFS
 
 
 class Snode:
@@ -312,8 +312,8 @@ class Splitter:
         tuple
             indices of the features selected
         """
-        mfs = MFS(max_features=max_features, discrete=False)
-        return mfs.cfs(dataset, labels).get_results()
+        mufs = MUFS(max_features=max_features, discrete=False)
+        return mufs.cfs(dataset, labels).get_results()
 
     @staticmethod
     def _fs_fcbf(
@@ -336,8 +336,8 @@ class Splitter:
         tuple
             indices of the features selected
         """
-        mfs = MFS(max_features=max_features, discrete=False)
-        return mfs.fcbf(dataset, labels, 5e-4).get_results()
+        mufs = MUFS(max_features=max_features, discrete=False)
+        return mufs.fcbf(dataset, labels, 5e-4).get_results()
 
     def partition_impurity(self, y: np.array) -> np.array:
         return self.criterion_function(y)

stree/__init__.py

@@ -1,6 +1,6 @@
 from .Strees import Stree, Siterator
 
-__version__ = "1.2"
+__version__ = "1.2.1"
 
 __author__ = "Ricardo Montañana Gómez"
 __copyright__ = "Copyright 2020-2021, Ricardo Montañana Gómez"